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update docs link & readme.md

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2020-09-24 13:45:26 +08:00
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8
docs/component/backtest.rst
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@@ -31,9 +31,9 @@ The simple example of the default strategy is as follows.
# pred_score is the prediction score
report, positions = backtest(pred_score, topk=50, n_drop=0.5, verbose=False, limit_threshold=0.0095)
To know more about backtesting with a specific strategy, please refer to `Strategy <strategy.html>`_.
To know more about backtesting with a specific ``Strategy``, please refer to `Strategy <strategy.html>`_.
To know more about the prediction score `pred_score` output by ``Model``, please refer to `Interday Model: Model Training & Prediction <model.html>`_.
To know more about the prediction score `pred_score` output by ``Interday Model``, please refer to `Interday Model: Model Training & Prediction <model.html>`_.
Prediction Score
-----------------
@@ -58,7 +58,7 @@ A prediction sample is shown as follows.
SH603133 2019-04-30 -0.302460
SZ300760 2019-04-30 -0.126383
``Model`` module can make predictions, please refer to `Model <model.html>`_.
``Interday Model`` module can make predictions, please refer to `Interday Model: Model Training & Prediction <model.html>`_.
Backtest Result
------------------
@@ -110,4 +110,4 @@ The backtest results are in the following form:
Reference
==============
To know more about ``Intraday Trading``, please refer to `Backtest API <../reference/api.html>`_.
To know more about ``Intraday Trading``, please refer to `Intraday Trading <../reference/api.html#module-qlib.contrib.evaluate>`_.

22
docs/component/data.rst
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@@ -124,9 +124,9 @@ Feature
- `ExpressionOps`
`ExpressionOps` will use operator for feature construction.
To know more about ``Operator``, please refer to `Operator API <../reference/api.html>`_.
To know more about ``Operator``, please refer to `Operator API <../reference/api.html#module-qlib.data.ops>`_.
To know more about ``Feature``, please refer to `Feature API <../reference/api.html>`_.
To know more about ``Feature``, please refer to `Feature API <../reference/api.html#module-qlib.data.base>`_.
Filter
-------------------
@@ -142,25 +142,25 @@ Filter
- `cross-sectional features filter` : rule_expression = '$rank($close)<10'
- `time-sequence features filter`: rule_expression = '$Ref($close, 3)>100'
To know more about ``Filter``, please refer to `Filter API <../reference/api.html>`_.
To know more about ``Filter``, please refer to `Filter API <../reference/api.html#module-qlib.data.filter>`_.
API
Reference
-------------
To know more about ``Data API``, please refer to `Data API <../reference/api.html>`_.
To know more about ``Data API``, please refer to `Data API <../reference/api.html#data>`_.
Data Handler
=================
Users can use ``Data Handler`` in an automatic workflow by ``Estimator``, refer to `Estimator <estimator.html>`_ for more details.
Users can use ``Data Handler`` in an automatic workflow by ``Estimator``, refer to `Estimator: Workflow Management <estimator.html>`_ for more details.
Also, ``Data Handler`` can be used as an independent module, by which users can easily preprocess data(standardization, remove NaN, etc.) and build datasets. It is a subclass of ``qlib.contrib.estimator.handler.BaseDataHandler``, which provides some interfaces as follows.
Base Class & Interface
----------------------
Qlib provides a base class `qlib.contrib.estimator.BaseDataHandler <../reference/api.html#class-qlib.contrib.estimator.BaseDataHandler>`_, which provides the following interfaces:
Qlib provides a base class `qlib.contrib.estimator.BaseDataHandler <../reference/api.html#qlib.contrib.estimator.handler.BaseDataHandler>`_, which provides the following interfaces:
- `setup_feature`
Implement the interface to load the data features.
@@ -204,7 +204,7 @@ Example
``Data Handler`` can be run with ``estimator`` by modifying the configuration file, and can also be used as a single module.
Know more about how to run ``Data Handler`` with ``estimator``, please refer to `Estimator <estimator.html#about-data>`_.
Know more about how to run ``Data Handler`` with ``Estimator``, please refer to `Estimator: Workflow Management <estimator.html>`_
Qlib provides implemented data handler `QLibDataHandlerClose`. The following example shows how to run `QLibDataHandlerV1` as a single module.
@@ -243,14 +243,14 @@ Qlib provides implemented data handler `QLibDataHandlerClose`. The following exa
print(x_train, y_train, x_validate, y_validate, x_test, y_test)
.. note:: (x_train, y_train, x_validate, y_validate, x_test, y_test) can be used as arguments for the ``fit``, ``predict``, and ``score`` methods of the 'Model' , please refer to `Model <model.html#Interface>`_.
.. note:: (x_train, y_train, x_validate, y_validate, x_test, y_test) can be used as arguments for the `fit`, `predic``, and `score` methods of the ``Interday Model`` , please refer to `Model <model.html#base-class-interface>`_.
Also, the above example has been given in ``examples.estimator.train_backtest_analyze.ipynb``.
API
---------
To know more about ``Data Handler``, please refer to `Data Handler API <../reference/api.html#handler>`_.
To know more about ``Data Handler``, please refer to `Data Handler API <../reference/api.html#module-qlib.contrib.estimator.handler>`_.
Cache
==========
@@ -336,5 +336,3 @@ We've specially designed a file structure to manage data and cache, please refer
- .index : an assorted index file recording the line index of all calendars
- ...
.. TODO: refer to paper

6
docs/component/estimator.rst
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@@ -279,7 +279,7 @@ Qlib supports custom models, but it must be a subclass of the `qlib.contrib.mode
The class `SomeModel` should be in the module `custom_model`, and ``Qlib`` could parse the `module_path` to load the class.
To know more about ``Model``, please refer to `Model <model.html>`_.
To know more about ``Interday Model``, please refer to `Interday Model: Training & Prediction <model.html>`_.
Data Section
-----------------
@@ -552,7 +552,7 @@ Users can specify `backtest` through a config file, for example:
Backtest initial cash, integer type. The `account` in `strategy` section is deprecated. It only works when `account` is not set in `backtest` section. It will be overridden by `account` in the `backtest` section. The default value is 1e9.
- `deal_price`
Order transaction price field, str type, the default value is vwap.
Order transaction price field, str type, the default value is close.
- `min_cost`
Min transaction cost, float type, the default value is 5.
@@ -586,7 +586,7 @@ Experiment Result
Form of Experimental Result
----------------------------
The result of the experiment is also the result of the ``Interdat Trading(Backtest)``, please refer to `Interday Trading <backtest.html>`_.
The result of the experiment is also the result of the ``Intraday Trading(Backtest)``, please refer to `Intraday Trading: Model&Strategy Testing <backtest.html>`_.
Get Experiment Result

7
docs/component/model.rst
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@@ -6,7 +6,7 @@ Interday Model: Model Training & Prediction
Introduction
===================
``Interday Model`` is designed to make the `prediction score` about stocks. Users can use the ``Interday Model`` in an automatic workflow by ``Estimator``, please refer to `Estimator <estimator.html>`_.
``Interday Model`` is designed to make the `prediction score` about stocks. Users can use the ``Interday Model`` in an automatic workflow by ``Estimator``, please refer to `Estimator: Workflow Management <estimator.html>`_.
Because the components in ``Qlib`` are designed in a loosely-coupled way, ``Interday Model`` can be used as an independent module also.
@@ -48,7 +48,7 @@ The base class provides the following interfaces:
.. note::
The number and names of the columns are determined by the data handler, please refer to `Data Handler <data.html#data-handler>`_ and `Estimator Data <estimator.html#about-data>`_.
The number and names of the columns are determined by the data handler, please refer to `Data Handler <data.html#data-handler>`_ and `Estimator Data Section <estimator.html#data-section>`_.
- `y_train`, pd.DataFrame type, train label
The following example explains the value of `y_train`:
@@ -117,7 +117,7 @@ Example
``Qlib`` provides ``LightGBM`` and ``DNN`` models as the baseline, the following steps show how to run`` LightGBM`` as an independent module.
- Initialize ``Qlib`` with `qlib.init` first, please refer to `initialization <../start/initialization.html>`_.
- Initialize ``Qlib`` with `qlib.init` first, please refer to `Initialization <../start/initialization.html>`_.
- Run the following code to get the `prediction score` `pred_score`
.. code-block:: Python
@@ -157,7 +157,6 @@ Example
"num_threads": 20,
}
# use default model
# custom Model, refer to: TODO: Model API url
model = LGBModel(**MODEL_CONFIG)
model.fit(x_train, y_train, x_validate, y_validate)
_pred = model.predict(x_test)

2
docs/component/report.rst
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@@ -100,7 +100,7 @@ Graphical Result
- Axis Y:
- `ic`
The `Pearson correlation coefficient` series between `label` and `prediction score`.
In the above example, the `label` is formulated as `Ref($close, -1)/$close - 1`. Please refer to `Data API Featrue <data.html>`_ for more details.
In the above example, the `label` is formulated as `Ref($close, -1)/$close - 1`. Please refer to `Data Featrue <data.html#feature>`_ for more details.
- `rank_ic`
The `Spearman's rank correlation coefficient` series between `label` and `prediction score`.

12
docs/component/strategy.rst
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@@ -7,7 +7,7 @@ Interday Strategy: Portfolio Management
Introduction
===================
``Interday Strategy`` is designed to adopt different trading strategies, which means that users can adopt different algorithms to generate investment portfolios based on the prediction scores of the ``Interday Model``. Users can use the ``Interday Strategy`` in an automatic workflow by ``Estimator``, please refer to `Estimator <estimator.html>`_.
``Interday Strategy`` is designed to adopt different trading strategies, which means that users can adopt different algorithms to generate investment portfolios based on the prediction scores of the ``Interday Model``. Users can use the ``Interday Strategy`` in an automatic workflow by ``Estimator``, please refer to `Estimator: Workflow Management <estimator.html>`_.
Because the components in ``Qlib`` are designed in a loosely-coupled way, ``Interday Strategy`` can be used as an independent module also.
@@ -95,11 +95,13 @@ Usage & Example
"limit_threshold": 0.095,
"account": 100000000,
"benchmark": BENCHMARK,
"deal_price": "vwap",
"deal_price": "close",
"open_cost": 0.0005,
"close_cost": 0.0015,
"min_cost": 5,
}
# use default strategy
# custom Strategy, refer to: TODO: Strategy API url
strategy = TopkDropoutStrategy(**STRATEGY_CONFIG)
# pred_score is the `prediction score` output by Model
@@ -115,4 +117,4 @@ To know more about ``Intraday Trading``, please refer to `Intraday Trading: Mode
Reference
===================
To know more about ``Interday Strategy``, please refer to `Strategy API <../reference/api.html>`_.
To know more about ``Interday Strategy``, please refer to `Strategy API <../reference/api.html#module-qlib.contrib.strategy.strategy>`_.